Numerous epidemiological studies demonstrate that sudden cardiac death, pulmonary embolism, myocardial infarction, and stroke have a 24-hour daily pattern with a broad peak between 9:00 to 11:00 AM. The mechanisms underlying this daily pattern are unknown. As an important first step to elucidate mechanisms, we propose an innovation that combines circadian cardiovascular physiology with analysis of a unique existing data set using novel statistical approaches that we have developed and shown to be sensitive indicators of cardiac dynamics and cardiac risk. Our primary mechanistic aim is to distinguish the separate effects upon cardiac dynamics of (i) the intrinsic circadian rhythm and (ii) the daily pattern in behavior related to the sleep-wake cycle and activity level. We will analyze existing data sets of four physiologically related variables (heart rate, blood pressure, temperature and activity level) that were simultaneously recorded in ostensibly healthy individuals throughout two complementary circadian protocols in which subjects' behaviors (including activity level and sleep-wake cycle) are controlled and the environment is constant. The protocols were: (i) a 10day Forced Desynchrony protocol (wherein subjects' sleep-wake cycles are adjusted to 28 hours so that their behaviors occur across all circadian phases) and (ii) a 38 hour constant routine protocol (wherein subjects remain awake and semi-recumbent). Core body temperature will be used as a circadian phase marker. From these data we will extract complementary statistical indices of dynamical structure and synchronization with our novel and sensitive analysis tools: (i) Detrended Fluctuation Analysis; (ii) Magnitude and Sign Analysis; (iii) Wavelet Transform; (iv) Hilbert Transform; (v) Fractal and Multifractal Analysis; (vi) Phase Synchronization Analysis. Analyses of these statistics in relation to the phase of the circadian rhythm, or separately the behavioral pattern may reveal cardiac dynamics related to the daily pattern of cardiovascular vulnerability. Furthermore, analysis of synchronization patterns among the physiologically related variables will enable us to deduce mechanistic links among variables that could underlie the cardiac dynamics. Our future aim (beyond this application) would be to determine whether the results in healthy individuals relate to patients with known cardiovascular risk.